The present invention relates generally to clutter rejection processors and processing methods and sensors, and more particularly, to a clutter rejection processor and processing method implemented using adaptive estimation of a clutter probability density function.
One prior art processing method uses a nearest distance classifier. The nearest distance classifier computes a normalized distance between the estimated target state and each stable track and forms a set of distances {di}. It then compares the set of distances to a preset threshold set on the basis of the covariance of the target state. If one or more tracks have a distance smaller than the threshold, the track with the smallest distance is presumed to be the target and is (re)acquired. The choice of the threshold is usually based on minimizing the probability of false match given the correct target track is being considered.
The disadvantage of the prior art nearest distance classifier is a potential high probability of false acquisition, particularly in situations where the covariance of the target state is large (due to poor knowledge of the target state) and the correct target is not detected. In this situation, many potential clutter and/or countermeasure tracks may have sufficiently small distance relative to the threshold to be falsely (re)acquired. The false (re)acquisition problem is particularly troublesome when a target track is not detected because the target track generally tends to be the smallest distance track to the estimated target state. Therefore, the presence of the target track tends to reduce false (re)acquisition.
Therefore, it would be an advantage to have a clutter rejection processor and processing method that does not have a high probability of false acquisition. Accordingly, it is an objective of the present invention to provide for a clutter rejection processor and processing method implemented using adaptive estimation of a clutter probability density function.